Fig. REF  illustrates initially detected objects along with their uncertainties. As shown in Fig. REF , detected objects with the lowest uncertainties are accurate, whereas, objects with the highest uncertainties are completely wrong. Therefore, we only target objects with medium uncertainties, which are neither perfectly detected, nor totally wrong.
We can take two advantages when focusing on improving uncertain objects: computational efficiency and performance gain. To analyze this, we compare our UAGDet to SGRN [1]}, which builds a context graph among objects for contextualizing them. Specifically, in SGRN [1]}, 50 edges per node are generated for every detected object. However, if SGRN [1]} is applied to DOTA [4]} with 1,250 initial object proposals, resulting graph contains 50\(\times \) 1250 edges for considering all objects' pair-wise interactions. Compared to such an approach, our UAGDet always generates 50\(\times \) 50 edges as a maximum under its analytical form, and usually generates between 50 to 1,000 edges in most cases. This is because we define the direction of edges only from source to target nodes, which results in much sparser graphs having advantages in terms of time and memory efficiency. Furthermore, we observe that uncertainty-aware graphs with edges from certain nodes to uncertain nodes are more valuable than pair-wise edges between all nodes in terms of performance. In particular, we compare the performances of two models – 73.1% for all pair-wise interactions, whereas 75.1% for our uncertainty-based interactions, on which we observe that ours largely outperforms the baseline. Also, the model with pair-wise edges among all nodes (73.1%) underperforms the RoITrans [5]} baseline (73.4%) that does not leverage the relational knowledge between objects. This result confirms that information propagation from uncertain objects to certain objects can harm the original detection results, which we prevent by constructing the bipartite directed graph.
